In this paper we describe a technical system for DC motor speed control.
The speed of DC motor is
controlled using Neural Network Based Model Reference and Predictive
controllers with the use of
Matlab/Simulink. The analysis of the DC motor is done with and without
input side Torque disturbance input and
the simulation results obtained by comparing the desired and actual
speed of the DC motor using random reference
and sinusoidal speed inputs for the DC motor with Model Reference
and Predictive controllers. The DC motor with
Model Reference controller shows almost the actual speed is the same
as the desired speed with a good performance
than the DC motor with Predictive controller for the system with and
without input side disturbance. Finally the
comparative simulation result prove the effectiveness of the DC motor
with Model Reference controller.
:C\:\\Users\\user\\Desktop\\NEW JOURNAL FOR ORCID - Copy\\NEW\\DC Motor Speed Control with the Presence of Input Disturbance using Neural Network Based Model Reference and Predictive Controllers.pdf:PDF
%0 Journal Article
%1 MustefaJibril12020d
%A Mustefa Jibril 1, Messay Tadese 2, Eliyas Alemayehu Tadese 3
%D 2020
%J Report and Opinion Journal
%K DC Model Network, Neural Predictive Reference controller controller, motor,
%N 8
%P 21-24
%R 10.7537/marsroj120820.06
%T DC Motor Speed Control with the Presence of Input Disturbance using
Neural Network Based Model
Reference and Predictive Controllers
%V 12
%X In this paper we describe a technical system for DC motor speed control.
The speed of DC motor is
controlled using Neural Network Based Model Reference and Predictive
controllers with the use of
Matlab/Simulink. The analysis of the DC motor is done with and without
input side Torque disturbance input and
the simulation results obtained by comparing the desired and actual
speed of the DC motor using random reference
and sinusoidal speed inputs for the DC motor with Model Reference
and Predictive controllers. The DC motor with
Model Reference controller shows almost the actual speed is the same
as the desired speed with a good performance
than the DC motor with Predictive controller for the system with and
without input side disturbance. Finally the
comparative simulation result prove the effectiveness of the DC motor
with Model Reference controller.
@article{MustefaJibril12020d,
abstract = {In this paper we describe a technical system for DC motor speed control.
The speed of DC motor is
controlled using Neural Network Based Model Reference and Predictive
controllers with the use of
Matlab/Simulink. The analysis of the DC motor is done with and without
input side Torque disturbance input and
the simulation results obtained by comparing the desired and actual
speed of the DC motor using random reference
and sinusoidal speed inputs for the DC motor with Model Reference
and Predictive controllers. The DC motor with
Model Reference controller shows almost the actual speed is the same
as the desired speed with a good performance
than the DC motor with Predictive controller for the system with and
without input side disturbance. Finally the
comparative simulation result prove the effectiveness of the DC motor
with Model Reference controller.},
added-at = {2020-09-01T13:58:46.000+0200},
author = {{Mustefa Jibril 1, Messay Tadese 2}, Eliyas Alemayehu Tadese 3},
biburl = {https://www.bibsonomy.org/bibtex/2867463c4971012261fc810eac8c2e883/mustefa1981},
doi = {10.7537/marsroj120820.06},
file = {:C\:\\Users\\user\\Desktop\\NEW JOURNAL FOR ORCID - Copy\\NEW\\DC Motor Speed Control with the Presence of Input Disturbance using Neural Network Based Model Reference and Predictive Controllers.pdf:PDF},
interhash = {fe7a1f226df5b53ec3db6465432a9464},
intrahash = {867463c4971012261fc810eac8c2e883},
journal = {Report and Opinion Journal},
keywords = {DC Model Network, Neural Predictive Reference controller controller, motor,},
month = {August},
number = 8,
owner = {user},
pages = {21-24},
review = {Peer Reviewed},
timestamp = {2020-09-01T13:59:01.000+0200},
title = {DC Motor Speed Control with the Presence of Input Disturbance using
Neural Network Based Model
Reference and Predictive Controllers},
volume = 12,
year = 2020
}